edisgo.io.timeseries_import

Module Contents

Functions

feedin_oedb_legacy(edisgo_object[, timeindex])

Import feed-in time series data for wind and solar power plants from the

feedin_oedb(edisgo_object, engine[, timeindex])

Import feed-in time series data for wind and solar power plants from the

load_time_series_demandlib(edisgo_obj[, timeindex])

Get normalized sectoral electricity load time series using the

cop_oedb(edisgo_object, engine, weather_cell_ids[, ...])

Get COP (coefficient of performance) time series data from the

heat_demand_oedb(edisgo_obj, scenario, engine[, timeindex])

Get heat demand profiles for heat pumps from the

electricity_demand_oedb(edisgo_obj, scenario, engine)

Get electricity demand profiles for all conventional loads from the

get_residential_heat_profiles_per_building(...)

Gets residential heat demand profiles per building.

get_district_heating_heat_demand_profiles(...)

Gets heat demand profiles of district heating networks from oedb.

get_cts_profiles_per_building(edisgo_obj, scenario, ...)

Gets CTS heat demand profiles per CTS building for all CTS buildings in MV grid.

get_cts_profiles_per_grid(bus_id, scenario, sector, engine)

Gets CTS heat or electricity demand profiles per building for all buildings in the

get_residential_electricity_profiles_per_building(...)

Gets residential electricity demand profiles per building.

get_industrial_electricity_profiles_per_site(site_ids, ...)

Gets industrial electricity demand profiles per site and OSM area.
edisgo.io.timeseries_import.feedin_oedb_legacy(edisgo_object, timeindex=None)[source]

Import feed-in time series data for wind and solar power plants from the OpenEnergy DataBase.

Parameters:

  • edisgo_obj (EDisGo) –

  • timeindex (pandas.DatetimeIndex or None) – Specifies time steps for which to return feed-in data. Leap years can currently not be handled. In case the given timeindex contains a leap year, the data will be indexed using the default year 2011 and returned for the whole year. If no timeindex is provided, the timeindex set in timeindex is used. If timeindex is not set, the data is indexed using the default year 2011 and returned for the whole year.

Returns:

DataFrame with hourly time series for active power feed-in per generator type (wind or solar, in column level 0) and weather cell (in column level 1), normalized to a capacity of 1 MW.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.feedin_oedb(edisgo_object, engine: sqlalchemy.engine.base.Engine, timeindex=None)[source]

Import feed-in time series data for wind and solar power plants from the OpenEnergy DataBase.

Parameters:

  • edisgo_obj (EDisGo) –

  • engine (sqlalchemy.Engine) – Database engine.

  • timeindex (pandas.DatetimeIndex or None) – Specifies time steps for which to return feed-in data. Leap years can currently not be handled. In case the given timeindex contains a leap year, the data will be indexed using the default year 2011 and returned for the whole year. If no timeindex is provided, the timeindex set in timeindex is used. If timeindex is not set, the data is indexed using the default year 2011 and returned for the whole year.

Returns:

DataFrame with hourly feed-in time series per generator type (wind or solar, in column level 0) and weather cell (in column level 1), normalized to a capacity of 1 MW. Index of the dataframe depends on parameter timeindex.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.load_time_series_demandlib(edisgo_obj, timeindex=None)[source]

Get normalized sectoral electricity load time series using the demandlib.

Resulting electricity load profiles hold time series of hourly conventional electricity demand for the sectors residential, cts, agricultural and industrial. Time series are normalized to a consumption of 1 MWh per year.

Parameters:

  • edisgo_obj (EDisGo) –

  • timeindex (pandas.DatetimeIndex or None) – Specifies time steps for which to return data. If no timeindex is provided, the timeindex set in timeindex is used. If timeindex is not set, the data is indexed using the default year 2011 and returned for the whole year.

Returns:

DataFrame with conventional electricity load time series for sectors residential, cts, agricultural and industrial. Index is a pandas.DatetimeIndex. Columns hold the sector type.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.cop_oedb(edisgo_object, engine, weather_cell_ids, timeindex=None)[source]

Get COP (coefficient of performance) time series data from the OpenEnergy DataBase.

Parameters:

  • edisgo_object (EDisGo) –

  • engine (sqlalchemy.Engine) – Database engine.

  • weather_cell_ids (list(int) or list(float)) – List (or array) of weather cell IDs to obtain COP data for.

  • timeindex (pandas.DatetimeIndex or None) – Specifies time steps for which to return data. Leap years can currently not be handled. In case the given timeindex contains a leap year, the data will be indexed using the default year 2011 and returned for the whole year. If no timeindex is provided, the timeindex set in timeindex is used. If timeindex is not set, the data is indexed using the default year 2011 and returned for the whole year.

Returns:

DataFrame with hourly COP time series in p.u. per weather cell. Index of the dataframe is a time index. Columns contain the weather cell ID as integer.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.heat_demand_oedb(edisgo_obj, scenario, engine, timeindex=None)[source]

Get heat demand profiles for heat pumps from the OpenEnergy DataBase.

Heat demand data is returned for all heat pumps in the grid. For more information on how individual heat demand profiles are obtained see functions get_residential_heat_profiles_per_building() and get_cts_profiles_per_building(). For more information on how district heating heat demand profiles are obtained see function get_district_heating_heat_demand_profiles().

Parameters:

  • edisgo_obj (EDisGo) –

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • engine (sqlalchemy.Engine) – Database engine.

  • timeindex (pandas.DatetimeIndex or None) – Specifies time steps for which to return data. Leap years can currently not be handled. In case the given timeindex contains a leap year, the data will be indexed using the default year (2035 in case of the ‘eGon2035’ and to 2045 in case of the ‘eGon100RE’ scenario) and returned for the whole year. If no timeindex is provided, the timeindex set in timeindex is used. If timeindex is not set, the data is indexed using the default year and returned for the whole year.

Returns:

DataFrame with hourly heat demand for one year in MW per heat pump. Index of the dataframe is a time index. Columns contain the heat pump name as in index of loads_df.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.electricity_demand_oedb(edisgo_obj, scenario, engine, timeindex=None, load_names=None)[source]

Get electricity demand profiles for all conventional loads from the OpenEnergy DataBase.

Conventional loads comprise conventional electricity applications in the residential, CTS and industrial sector. For more information on how the demand profiles are obtained see functions get_residential_electricity_profiles_per_building(), get_cts_profiles_per_building() and get_industrial_electricity_profiles_per_site().

Parameters:

  • edisgo_obj (EDisGo) –

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • engine (sqlalchemy.Engine) – Database engine.

  • timeindex (pandas.DatetimeIndex or None) – Specifies time steps for which to return data. Leap years can currently not be handled. In case the given timeindex contains a leap year, the data will be indexed using the default year (2035 in case of the ‘eGon2035’ and to 2045 in case of the ‘eGon100RE’ scenario) and returned for the whole year. If no timeindex is provided, the timeindex set in timeindex is used. If timeindex is not set, the data is indexed using the default year and returned for the whole year.

  • load_names (list(str) or None) – Conventional loads (as in index of loads_df) for which to retrieve electricity demand time series. If none are provided, profiles for all conventional loads are returned.

Returns:

DataFrame with hourly electricity demand for one year in MW per conventional load. Index of the dataframe is a time index. Columns contain the load name as in index of loads_df.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.get_residential_heat_profiles_per_building(building_ids, scenario, engine)[source]

Gets residential heat demand profiles per building.

Parameters:

  • building_ids (list(int)) – List of building IDs to retrieve heat demand profiles for.

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • engine (sqlalchemy.Engine) – Database engine.

Returns:

Dataframe with residential heat demand profiles per building for one year in an hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and column names are building ID as integer.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.get_district_heating_heat_demand_profiles(district_heating_ids, scenario, engine)[source]

Gets heat demand profiles of district heating networks from oedb.

Parameters:

  • district_heating_ids (list(int)) – List of district heating area IDs to get heat demand profiles for.

  • scenario (str) – Scenario for which to retrieve data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • engine (sqlalchemy.Engine) – Database engine.

Returns:

Dataframe with heat demand profiles per district heating network for one year in an hourly resolution in MW. Index contains hour of the year (from 1 to 8760) and column names are district heating network ID as integer.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.get_cts_profiles_per_building(edisgo_obj, scenario, sector, engine)[source]

Gets CTS heat demand profiles per CTS building for all CTS buildings in MV grid.

This function is a helper function that should not be but is necessary, as in egon_data buildings are mapped to a grid based on the zensus cell they are in whereas in ding0 buildings are mapped to a grid based on the geolocation. As it can happen that buildings lie outside an MV grid but within a zensus cell that is assigned to that MV grid, they are mapped differently in egon_data and ding0. This function therefore checks, if there are CTS loads with other grid IDs and if so, gets profiles for other grid IDs (by calling get_cts_profiles_per_grid() with different grid IDs) in order to obtain a demand profile for all CTS loads.

Parameters:

  • edisgo_obj (EDisGo) –

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • sector (str) – Demand sector for which profile is calculated: “electricity” or “heat”

  • engine (sqlalchemy.Engine) – Database engine.

Returns:

Dataframe with CTS demand profiles per building for one year in an hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and column names are building ID as integer.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.get_cts_profiles_per_grid(bus_id, scenario, sector, engine)[source]

Gets CTS heat or electricity demand profiles per building for all buildings in the given MV grid.

Parameters:

  • bus_id (int) – MV grid ID.

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • sector (str) – Demand sector for which profile is calculated: “electricity” or “heat”

  • engine (sqlalchemy.Engine) – Database engine.

Returns:

Dataframe with CTS demand profiles per building for one year in an hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and column names are building ID as integer.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.get_residential_electricity_profiles_per_building(building_ids, scenario, engine)[source]

Gets residential electricity demand profiles per building.

Parameters:

  • building_ids (list(int)) – List of building IDs to retrieve electricity demand profiles for.

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • engine (sqlalchemy.Engine) – Database engine.

Returns:

Dataframe with residential electricity demand profiles per building for one year in an hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and column names are building ID as integer.

Return type:

pandas.DataFrame

edisgo.io.timeseries_import.get_industrial_electricity_profiles_per_site(site_ids, scenario, engine)[source]

Gets industrial electricity demand profiles per site and OSM area.

Parameters:

  • site_ids (list(int)) – List of industrial site and OSM IDs to retrieve electricity demand profiles for.

  • scenario (str) – Scenario for which to retrieve demand data. Possible options are ‘eGon2035’ and ‘eGon100RE’.

  • engine (sqlalchemy.Engine) – Database engine.

Returns:

Dataframe with industrial electricity demand profiles per site and OSM area for one year in an hourly resolution in MW. Index contains hour of the year (from 0 to 8759) and column names are site ID as integer.

Return type:

pandas.DataFrame